Talend Big Data Integration Training Course

This instructor-led live training in South Korea (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.

By the end of this training, participants will be able to:

• Gain a deep understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance across various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led, live training in South Korea (online or onsite) is aimed at beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.

By the end of this training, participants will be able to:

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in different environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led live training in South Korea (online or onsite) targets intermediate-level data scientists and engineers who want to apply Google Colab and Apache Spark for big data processing and analytics.

By the conclusion of this training, participants will be able to:

• Establish a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Apache NiFi is an open-source platform for data integration and event processing that utilizes a flow-based architecture. It facilitates the automated, real-time routing, transformation, and mediation of data between diverse systems, featuring a web-based user interface and granular control capabilities.

This instructor-led live training, available either onsite or remotely, is designed for intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows within production environments.

Upon completing this training, participants will be capable of:

• Installing, configuring, and maintaining Apache NiFi clusters.
• Designing and managing dataflows originating from various sources and destinations.
• Implementing logic for flow automation, routing, and transformation.
• Optimizing performance, monitoring operations, and resolving issues.

Course Format

• Interactive lectures accompanied by discussions on real-world architecture.
• Practical labs focused on building, deploying, and managing dataflows.
• Scenario-based exercises conducted in a live-lab environment.

Course Customization Options

• For inquiries regarding customized training for this course, please reach out to us to arrange details.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Participants will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently handle large datasets by applying distributed computing principles.

This instructor-led, live training in South Korea (online or onsite) is designed for engineers who wish to set up and deploy Apache Spark systems for processing very large amounts of data.

By the end of this training, participants will be able to:

• Install and configure Apache Spark.
• Quickly process and analyze very large data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

This instructor-led live training in South Korea (online or on-site) targets beginner to intermediate-level system administrators looking to deploy, maintain, and optimize Spark clusters.

By the end of this training, participants will be able to:

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

While the initial learning curve for Apache Spark can be steep, requiring significant effort to see early results, this course is designed to help you quickly overcome that initial hurdle. By the end of the training, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and gain proficiency with both Python and Scala APIs. You will also develop a solid understanding of executors, tasks, and other core components. Additionally, the course emphasizes best practices for cloud deployment, with a strong focus on Databricks and AWS. Students will explore the differences between AWS EMR and AWS Glue, one of AWS's newer Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in South Korea, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.

By the end of this training, participants will be able to:

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in South Korea (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

By the end of this training, participants will be able to:

• Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
• Understand the features, core components, and architecture of Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for big data processing.
• Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.

Stratio offers a data-centric platform that seamlessly integrates big data, artificial intelligence, and governance into a unified solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led live training, available both online and onsite, targets intermediate-level data professionals looking to effectively utilize Stratio's Rocket and Intelligence modules with PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and complex data logic.

Upon completion of this training, participants will be equipped to:

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analysis.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations within PySpark.

Course Format

• Engaging lectures and interactive discussions.
• Numerous exercises and hands-on practice sessions.
• Practical implementation exercises in a live laboratory environment.

Customization Options

• For tailored training requests, please contact us to arrange specific requirements.

This instructor-led live training, available in South Korea (online or onsite), is designed for system administrators, data scientists, and business analysts who wish to set up Talend Administration Center to deploy and manage organizational roles and tasks.

Upon completing this training, participants will be able to:

• Install and configure Talend Administration Center.
• Understand and apply fundamental Talend management principles.
• Create, deploy, and execute business projects or tasks within Talend.
• Monitor dataset security and establish business routines aligned with the TAC framework.
• Gain a comprehensive understanding of big data applications.

This instructor-led, live training in South Korea (online or onsite) is aimed at beginner to intermediate-level data analysts who wish to deepen their understanding and skills in managing and improving data quality using Talend Data Stewardship.

By the end of this training, participants will be able to:

• Gain a comprehensive understanding of the role of data stewardship in maintaining data quality.
• Use Talend Data Stewardship for managing data quality tasks.
• Create, assign, and manage tasks within Talend Data Stewardship, including workflow customization.
• Use the tool's reporting and monitoring capabilities to track data quality and stewardship efforts.

In this instructor-led live training at South Korea, participants will learn how to use Talend Open Studio for ESB to create, connect, mediate, and manage services and their interactions.

Upon completion of this training, participants will be able to

• Integrate, enhance, and deploy ESB technologies as unified packages across various deployment environments.
• Understand and effectively use the most commonly used components of Talend Open Studio.
• Integrate any application, database, API, or Web services.
• Seamlessly connect heterogeneous systems and applications.
• Embed existing Java code libraries to extend project capabilities.
• Leverage community-driven components and code to extend projects.
• Rapidly integrate systems, applications, and data sources within a drag-and-drop Eclipse environment.
• Reduce development time and maintenance costs by generating optimized, reusable code.